Open AccessRobust Facial Expression Recognition Based on Local Directional Pattern
Author(s) -
Jabid Taskeed,
Kabir Md. Hasanul,
Chae Oksam
Publication year - 2010
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.10.1510.0132
Subject(s) - artificial intelligence , pattern recognition (psychology) , adaboost , computer science , feature (linguistics) , local binary patterns , support vector machine , dimensionality reduction , facial expression , enhanced data rates for gsm evolution , principal component analysis , expression (computer science) , feature extraction , image (mathematics) , encoding (memory) , feature vector , curse of dimensionality , pixel , histogram , programming language , philosophy , linguistics
Automatic facial expression recognition has many potential applications in different areas of human computer interaction. However, they are not yet fully realized due to the lack of an effective facial feature descriptor. In this paper, we present a new appearance‐based feature descriptor, the local directional pattern (LDP), to represent facial geometry and analyze its performance in expression recognition. An LDP feature is obtained by computing the edge response values in 8 directions at each pixel and encoding them into an 8 bit binary number using the relative strength of these edge responses. The LDP descriptor, a distribution of LDP codes within an image or image patch, is used to describe each expression image. The effectiveness of dimensionality reduction techniques, such as principal component analysis and AdaBoost, is also analyzed in terms of computational cost saving and classification accuracy. Two well‐known machine learning methods, template matching and support vector machine, are used for classification using the Cohn‐Kanade and Japanese female facial expression databases. Better classification accuracy shows the superiority of LDP descriptor against other appearance‐based feature descriptors.